In the present disclosure, where a document, an act and/or an item of knowledge is referred to and/or discussed, then such reference and/or discussion is not an admission that the document, the act and/or the item of knowledge and/or any combination thereof was at the priority date, publicly available, known to the public, part of common general knowledge and/or otherwise constitutes prior art under the applicable statutory provisions; and/or is known to be relevant to an attempt to solve any problem with which the present disclosure is concerned with. Further, nothing is disclaimed.
Pathology is a field of science which deals with laboratory examinations of samples of tissue for diagnostic, research, forensic, academic, or other purposes. Pathologists, as well others working in medical or biological research, examine human, animal, floral, or other tissue to assess disease state or understand biological features. In one aspect of pathology, a tissue is biopsied from a patient for analysis and fixed on a glass slide to be viewed under a light microscope. However, such form of tissue assessment is disadvantageous for various reasons. For example, some of such disadvantages include subjectivity and low throughput.
With respect to the subjectivity, a purely human analysis of tissue suffers from low inter-pathologist concordance rates and low intra-pathologist concordance rates. The inter-pathologist concordance refers to an ability of different human pathologists to agree on an assessment (typically diagnostic or prognostic assessment) of a same biopsy. The intra-pathologist concordance refers to an ability of a single pathologist to reproduce his/her assessment of a same tissue section at two separate times. For example, according to Hu, Fei, Nikita V. Orlav, and Ilya G. Goldberg, Telehealthcare Computing and Engineering: Principles and Design, 2013, Print, “Traditional pathology is based on manual assessments of tissue sections under a microscope. A common problem with that approach is the inconsistency of readings across different readers and even by the same reader. Another common problem is that a high-quality diagnosis often requires analyzing multiple samples from the same patient independently, which is challenging with a single reader. Manual readers faced with many similar samples can also experience fatigue, adding to the inconsistency of the readings.” (Hu, Fei, Nikita V. Orlav, and Ilya G. Goldberg. Telehealthcare Computing and Engineering: Principles and Design).
With respect to the low throughput, an unnecessary amount of time spent on analysis of tissue by a human reader. For example, according to Gurcan, Metin N. et al. “Histopathological Image Analysis: A Review.” IEEE reviews in biomedical engineering 2 (2009): 147-171. PMC. Web. 24 Feb. 2015, “approximately 80% of the 1 million prostate biopsies performed in the US every year are benign; this suggests that prostate pathologists are spending 80% of their time sieving through benign tissue”. However, in a past decade, digitally scanned slides have enabled computer-based technologies to be applied to pathology. By scanning tissue slides into digital images using specialized scanners, some aspects of storage, viewing, analysis, and assessment of tissue samples can be aided by digital approaches.
The past decade has brought promising developments in pathology. While those in biology or medicine have been restricted to the light microscope for inspection of glass slides, whole-slide imaging (WSI) has offered a radically different medium for understanding human tissue or other biological tissues. WSI scanners create high-resolution digital images of glass slides and differ from cameras attached to or integrated with microscopes in that, while a camera captures a static snapshot of a slide of a single area at a fixed magnification, a WSI scanner scans across the slide at every magnification power (typically up to 40×), and stitches together various images into terabyte-scale files. Accordingly, various implications of slide digitization in pathology are profound and extend beyond one or more benefits of simply migrating workflows from an optical lens to a display. Technology and research are being applied to many components of such digital workflow chain. Though currently largely fragmented and underdeveloped, this digital workflow chain includes scanning, storage (and cloud migration), viewing and integration, computing/processing, image analysis, machine learning, and diagnostics.
While WSI scanning is peaking in technological maturity, an infrastructure for moving, storing, accessing, and processing medical image as large as biopsy scans is weak. For example, server infrastructure to manage digital pathology data is costly. Therefore, in order to store many terabytes of medical images entails additional information technology (IT) administrators, software for management and retrieval, servers, redundant storage clusters, data, backup, security, and recovery services, many of which can cost millions of dollars per year. Since hospitals and commercial pathology labs are not IT businesses, there is a need for computing systems which can provide cloud storage and image access systems at orders of magnitude cheaper than currently available in-house alternatives.
Additionally, while some analysis methods for tissue images may be mathematically and histologically sound, there remain some logistical or implementational problems. For example, due to a fact that some digital biopsy images are in extremely high-resolution and therefore, large in units of information, in many instances, such images mandate several gigabytes of storage space. Also, another consequence of such large image size is a significant processing time involved to perform some quantitative analysis. Therefore, long processing times may preclude conclusions from having any clinical significance, where the conclusions may be drawn from a quantitative image analysis. Therefore, some of such quantitative analysis is desired to be performed in real-time, or in near real-time. Further, due to a high number of biopsies excised, stained, and scanned per year, a computing system that aims to serve as a clinical aid should be able to process a large volume of images at any given time, without suffering significant performance drops. Additionally, some results of the quantitative image analysis may lose utility or value if such results are discarded after calculation. If the quantitative image analysis results are subsequently not stored in an effective way, then, if the computing system employs models, then such models cannot be effectively trained to make clinical judgments. Therefore, there is a need for a robust, well-defined, and highly accessible computing system which is capable of storing quantitative analysis results in an efficient manner.